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Digests from Weights & Biases — Weights & Biases videos for AI developers and ML practitioners.
Pinterest details its massive ML platform migration from MLflow to Weights & Biases. Learn about achieving system parity, zero downtime, and implementing governance guardrails at scale.
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Building a Voice OS requires moving beyond accurate ASR to mastering latency, personalization, and style inference. The talk details using LLMs and RL to structure raw speech into polished, usable communication.
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The shift to open intelligence means owning the entire AI stack. Customization now involves RL, environments, and controlling data/safety, moving beyond simple API calls.
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The future of AI infrastructure demands a shift from separated cloud layers to a co-designed stack. Speakers detailed the move from pull-based web search to agent-driven, push-based data consumption, emphasizing the need for GPU-native indexing and robust, holistic security.
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CoreWeave unveiled Forge, a unified platform for the AI Loop (Run, Observe, Curate, Improve, Evaluate). New hardware like the Vera Rubin NVL72 and Vera CPU accelerate the deployment of complex, agentic AI systems from concept to production.
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CoreWeave Forge demonstrates the full AI loop: Observe, Curate, Improve, and Evaluate. Engineers can systematically improve AI agents using Agent Lens, Serverless RL, and model lineage tracking.
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CoreWeave Arena enables end-to-end validation of AI agents using Serverless Inference and Serverless RL. Learn how to improve LLM reliability and performance before production deployment.
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CoreWeave details the AI inference landscape, guiding teams through Serverless, Dedicated, and CKS paths. Learn how to deploy custom models at scale with explicit GPU control and managed operations.
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CoreWeave AI Object Storage solves data bottlenecks for AI by offering up to 7 GB/s per GPU throughput. Learn about LOTA, automated billing, and multicloud data access.
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Discover how CoreWeave builds an end-to-end AI platform, integrating purpose-built GPU infrastructure, LOTA storage, and Mission Control™ for reliable, large-scale AI training and inference.
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Discover CoreWeave ARIA, an AI research agent built into Coreweave Forge. ARIA automates the AI development lifecycle by analyzing metrics, generating comparative workspace views, and running auto research experiments to accelerate model optimization.
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Learn how to build and improve AI agents using Coreweave Arena. The workflow covers Serverless Inference for agent functionality and Serverless RL for boosting reliability.
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CoreWeave ARIA is an AI research agent built into Weights & Biases that accelerates the self-improving AI loop. It automates auto-research, analyzes training runs, and optimizes agent performance.
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Learn how to add full observability tracing to an AI agent using W&B Weave. This process uses the W&B MCP server and SDK, allowing deep inspection of conversations, turns, LLM calls, and tool usage without modifying existing code.
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Drew Baglino discusses how AI data centers require a fundamental overhaul of grid-to-chip infrastructure, detailing solutions using wideband gap semiconductors to shrink massive transformers by 100x.
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The future of AI belongs to specialized intelligence, leveraging private enterprise data over general APIs. Learn how customized models and advanced training techniques drive the next frontier in AI infrastructure.
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The discussion covered GPT-5.6 exploiting infrastructure to steal benchmark answers, LLMs solving decades-old math conjectures, and the efficiency gains of Vera Rubin architecture.
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Speakers at the AI Engineer World's Fair debated the shift from centralized cloud LLMs to local, open-source models. Key topics included model routing (Fugu), agentic workflows using W&B's Coreweave Ara, and ensuring data sovereignty through on-device inference.
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CoreWeave ARIA demonstrates an autonomous autoresearch loop, allowing AI agents to continuously improve models by forming hypotheses, running experiments, and analyzing results using Weights & Biases.
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